Grok's Mixture of Agents: Easy Setup and Interface

Grok released a native mixture of agents, enabling less capable models to perform nearly at GPT-3.0 level, with a streamlined setup process.

### Setting Up Grok's Mixture of Agents Interface for Enhanced Model Capabilities
* 🧠  Grok has released a new tool that allows users to combine multiple models to achieve **near-GPT-3.0 capabilities**, called **Mixture of Agents**.
* 🔑  To set up this interface, users need **Visual Studio Code** and a **Grok API key**.
* 📦  The project repository can be cloned from GitHub using **`git clone [repository URL]`**.
* ⚙️  After navigating to the project directory, a Conda environment is created with **`conda create -n gr-MOA python=3.11`**.
* 🔐  Users then need to **set their Grok API key** in a `.env` file within the project directory.
* 📊  The interface allows users to **select main models**, **adjust the number of layers**, and **customize agents for each layer**.
* ⚡️  The tool is highly efficient, producing **accurate results quickly** even with **multiple layers of agents**.
* 🌐  The interface also includes **deployment options** and various settings for **user customization**.

so Grok just dropped mixture of agents natively. For those of you who don't remember, mixture of agents allows you to take less capable models and make them incredibly capable, nearly GPT-3.0 level. I made an entire video about exactly how it works. I'll link that in the description below, but today I'm going to show you how to set it all up, and it's actually really easy. So this video is going to be on the shorter side. The only thing you're going to need is VSS code and a Grok API key. First, open up VSS code. Then you're going to CD into whatever directory you like to store your project. So for me, when I'm playing around with it, I like to go to the desktop. Now here's the project repository, and it was created by Sai from Grok. So thank you so much to him for putting this together. It is so much better than my hacky version that I put together, and it comes with an interface, so it is especially good.

is how mixture of Agents Works. Here's the prompt: it goes through multiple agents work together over multiple layers to come up with the best possible output. Now when I first read this paper, what came to mind immediately is it's probably pretty slow, and they actually pointed that out in the paper. But of course, immediately my mind went to: Let's power this with grock, because then you have the massive speed Advantage. So I will drop the link to this in the description below. So back to VSS code, you're going to type git clone, and then the GitHub URL for this project. And switching back, you can find it right here. So if you click this little green code button, there's a little copy button right there. Then you're going to hit enter. Once that's done, let's CD into grock MOA. Now we're in that project directory. Next, you're going to want to click this explore button, open folder, go to your desktop, and then click that new project that you just downloaded, and click open. Okay, now we have it open.

Let's open up the terminal down here again. The next thing we're going to do is create a new Conda environment. So, type `conda create -n gr-MOA python=3.11`, then hit enter. Hit enter again to confirm. And then when it's done, you're going to grab this command right here to activate it. Copy and paste it into the terminal, and then hit enter. You can see it's active because it says so right there. Next, we need to install all of the dependencies. So, we're going to type `pip install -r requirements.txt`, then hit enter. I didn't run into a single problem while getting this spun up, so hopefully you don't either. And it also looks like it comes with a Docker file if you wanted to use that. Next, you're going to right-click in this project directory and hit new file, and type `.env` because we need to set our environment variables. From there, it's just going to be one line: `GR_API_KEY=` and then you place your Grock API key right

here now, if you don't already have a Grok account, go ahead and sign up. Go to console.grok.com. Keys, create key. We're going to type M MOA YT so I know it's for YouTube. Hit enter. I am going to revoke this key before publishing the video. Let's copy, switch back to VS Code, paste it in, and hit save. And we're pretty much done. So last again, get it up and running. You're going to type streamlit run app.py and then hit enter. All right, then it opens up localhost. So this is running locally. It'll take a second to spin up, and there we go, this is the interface. So let me give you a demo of it really quickly. So on the left side, this is where we can have all of our settings, and we can select our main model. And we probably want the most capable model, so right now, Llama 370b sounds good. We can actually subtract and add number of layers, so you can experiment with the number of layers that works best for your use case now.

according to the paper, three layers seems to be optimal, and that is the default for this project. So we're going to leave it at three. But of course, if you want to play around with it and experiment, see what works for you, please do. Then we have the main model temperature. I am going to leave it where it is, but again, something else you can experiment with. And here is where we can actually customize the agents for each layer. So we have layer agent one, layer agent two, and layer agent three. We're going to be using Lama 38b, Gemma 7B, and then Lama 38b again. And of course, Grock has other models that you can play around with. And you would just change the model name right there. And you can also change the temperature and other settings as you see fit. So with that set, let's give it a try. Write 10 sentences that end with the word Apple. Now it is going through multiple layers with multiple agents, but still look how fast that is. And the cool thing is you can

actually dig into each layer and each agent to see what they've done. So here's layer 1, agent one. Here's the output, and it looks like layer 1, agent one actually got it right right out of the box. Layer One, agent two thought and response, so that is a really poor response, and that just happens to be Gemma. And then agent three looks like it almost got it right, but here's one sentence that it did not. So you can see that was layer one. Now we have layer two of the three agents and layer three. And then at the end of it, we have llama 370b putting it all together, and it got the right answer, and it was super fast too. So really cool project out of Gro. Thank you for making mixture of Agents native to Gro. Now I actually hope they build this into the main interface as an option, and I hope inference companies actually start building a lot of these types of things mixture of Agents route llm Chain of Thought all of these things should be built into the inference interface.

couple last things up in the top right. We have a deploy button. If you wanted to deploy this with Streamlit, makes it very easy. We also, under the three dots, have rerun, which we can do just like that. We also have different settings: run on Save, wide mode, and the app Theme. You can print, you can record a screencast, which I wasn't expecting to be in here, and you can also clear the cache. So a lot of cool things. I have a feeling this project is going to evolve quite a bit, so I'm really excited to see it. So check it out. I'll drop all the links in the description below. If you liked this video, please consider giving a like and subscribe, and I'll see you in the next one.

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Setting Up Mixture of Agents with Grok: A Quick Guide

Grok has released a new mixture of agents that can significantly enhance the capability of models, approaching GPT-3.

Setting it up is easy, requiring only VSS code and a Grok API key.

The project repository, created by Sai from Grok, comes with a user-friendly interface, offering a speed advantage to the potentially slow process of mixture of agents.

The setup process involves cloning the GitHub project and opening it in VSS code.

Setting Up a New Conda Environment and Running a Python Application

To create a new Conda environment, use the command `conda create -n gr-MOA python=3.

Install dependencies with `pip install -r requirements.

Finally, run the application using `streamlit run app.

py` to access the interface and demo the settings and model selection.

Customizing Agents for Multi-Layered Models

The default setting for the project involves three layers, with the option to experiment with different configurations.

The main model temperature is also customizable for further experimentation.

The project uses specific agents for each layer, namely Lama 38b, Gemma 7B, and Lama 38b.

Now, let's give it a try and write 10 sentences that end with the word Apple.

The process involves going through multiple layers with multiple agents, but still, it is remarkably fast.

Each layer and agent's output can be analyzed, showing the effectiveness of each one.

Finally, the project combines the results using Lama 370b, resulting in the correct answer, and it was exceptionally fast.

This project, developed by Gro, is a really cool demonstration of the mixture of agents, and there is hope for it to be integrated into the main interface as an option, potentially influencing other inference companies to follow suit.

Streamlit offers a deploy button for easy deployment, a rerun option, and various settings like run on Save and wide mode.

The platform also provides the ability to print, record a screencast, and clear the cache, making it versatile for different needs.